Choosing the Best AI Agent Framework in 2025
A clear, practical comparison of the leading agent frameworks in 2025, with insights on UI capabilities, deployment flexibility, observability, and cloud lock-in. Ideal for anyone evaluating the right foundation for real-world AI products.
Introduction
Over the past months at FASHN, we have been researching the best technologies for building and scaling our app's AI Agent. This is the agent that now powers the experience on our dashboard homepage. What began as an exploration of how to improve our agent architecture evolved into a full evaluation of the rapidly growing agent-framework ecosystem.
This article summarizes that research. It compares the top frameworks available in 2025 and highlights what matters most when choosing the foundation for a production agent system.
All frameworks were evaluated using the same criteria.
Evaluation Criteria
1. Language Support
Whether the framework supports TypeScript, Python, or multiple languages. This affects team adoption, ecosystem compatibility, and how easily developers can integrate the framework into existing codebases.
2. UI Integration
How well the framework supports building user interfaces. This includes chat components, React or Next.js support, streaming UX, and the availability of UI primitives for agent interactions.
3. Deployment Options
Where and how the framework can run. This includes support for serverless runtimes, Docker, or managed cloud platforms. Deployment flexibility determines scalability, cost, and portability.
4. LLM Provider Flexibility
Whether the framework locks you into one model provider or allows you to switch between OpenAI, Anthropic, Google, or local models. Multi-provider support reduces risk and makes architectures more future-proof.
5. MCP Support
Compatibility with the Model Context Protocol, either as a client or server. MCP support enables tighter integrations with tools, external systems, and multi-agent interactions.
6. Maturity and Adoption
How stable the framework is, how frequently it is updated, and how widely it is used in the industry. Mature frameworks tend to have better documentation, fewer breaking changes, and stronger community support.
7. Observability
The level of visibility the framework provides into agent behavior: logs, traces, debugging tools, evaluations, or integrations with observability platforms. Essential for monitoring and improving agent reliability.
8. Cloud Lock-in Risk
How dependent the framework is on a proprietary or managed cloud environment. Higher lock-in means less flexibility, harder migration, and greater reliance on a single vendor’s roadmap.
Framework Comparisons
With the evaluation criteria defined, we can now look at how each framework actually performs. The following breakdown highlights what each one offers, where it fits best, and the tradeoffs to consider when building production-grade agents.
AI SDK (Vercel) - Best for Product Teams, UI, and React
What it is
AI SDK is a flexible TypeScript toolkit designed for building AI-powered applications with first-class support for React and Next.js. It gives developers low-level primitives for agents, streaming, and tool calls and pairs these with the strongest UI ecosystem available today.
Source: https://ai-sdk.dev/
Language Support
- TypeScript only
UI Integration
- Industry leading UI via AI Elements
- Deep React and Next.js support
- React Native support using Expo
- Support Vue,js and Svelte
- Best frontend experience across all frameworks
Deployment Options
- Native to Next.js and Vercel
- Works on Node and serverless environments
- Can be embedded directly in existing products without new infrastructure
LLM Providers
- Multi-provider
MCP Support
- MCP client support
Maturity and Adoption
- Backed by Vercel
- Very mature and stable
- Strong community and rapid development
Observability
- No native observability
- Integrates with Langfuse, LangSmith, Traceloop, Weave, Helicone, Axiom, etc.
Cloud Lock-in Risk
- None. You deploy where you want and keep full control.
Best for
Teams building UI-heavy AI products, specially with React or Next.js.
Key Takeaways
AI SDK stands out for its unmatched UI capabilities and seamless React and Next.js integration, making it the most natural choice for products that require rich, interactive, human-in-the-loop experiences. It offers flexible deployment options and strong developer ergonomics, with the main tradeoff being that it remains a low-level toolkit, requiring teams to assemble many pieces themselves, and that it depends on third-party observability tools.
Mastra - TypeScript-First Agent Framework
What it is
Mastra is a TypeScript framework that provides built-in building blocks for agents, tools, RAG pipelines, workflows, memory, and orchestration. It sits above AI SDK in abstraction and accelerates backend agent development.
Source: https://mastra.ai/
Language Support
- TypeScript only
UI Integration
- No built-in UI kit
- Integrates easily with AI SDK for UI
Deployment Options
- Mastra Cloud: fully managed platform with zero-config deployments, GitHub integration, built-in logs, and traces
- Node.js servers
- Serverless environments
- Docker
- Cloud provider deployments (AWS, Azure, DigitalOcean, etc.)
LLM Providers
- Multi-provider
MCP Support
- Client and server support
Maturity and Adoption
- Fast-growing in the TS ecosystem
- Active releases and strong developer experience
Observability
- Provides logging and monitoring helpers
- Supports third-party observability integrations
Cloud Lock-in Risk
- Moderate if using Mastra Cloud
- None if self-hosting
Best for
Teams that want a TypeScript agent framework with structured primitives and faster backend development.
Key Takeaways
Mastra offers a well-structured, TypeScript-native approach to building agents, with flexible deployment options and the convenience of a fully managed Mastra Cloud environment when needed. Although it lacks built-in UI components, it pairs well with AI SDK and provides a solid developer experience for backend-focused agent architectures.
LangGraph and LangChain - Workflow and Orchestration Powerhouse
What it is
LangGraph is a graph-based orchestration system for designing complex, stateful, multi-step agent workflows. It is built for durable execution, retries, branching logic, and long-running automations.
LangChain provides the core building blocks for LLM applications, while LangGraph sits on top of it and adds stateful, graph-based orchestration. LangChain is best for linear or simple chains, and LangGraph is designed for complex, adaptive, long-running workflows.
Source: https://www.langchain.com/langgraph
Language Support
- Python and TypeScript
UI Integration
- No built-in UI
- Requires external UI frameworks
Deployment Options
- Strongest deployment through LangSmith Cloud
- Self-hosting is possible but poorly documented
- Some workflows require LangSmith API keys
LLM Providers
- Multi-provider
MCP Support
- Supports MCP clients
Maturity and Adoption
- Very large ecosystem
- LangChain is widely adopted, even though it is not highly recommended
- LangGraph evolving rapidly, but still in its early days
Observability
- Exceptional through LangSmith
- Deep traces, debugging, evaluations, and dataset management
Cloud Lock-in Risk
- High, since LangSmith Cloud is heavily integrated and required for many features
Best for
Teams building complex workflows, long-running agents, automation systems, or enterprise orchestration.
Key Takeaways
LangChain provides the core building blocks for LLM applications, while LangGraph adds the orchestration needed for complex, stateful, multi-step workflows. Together they are powerful for automation and enterprise systems, with excellent observability through LangSmith. However, the graph-based model introduces additional complexity, leading to a heavier developer experience compared to more straightforward, code-driven agent frameworks.
Agno - Python Multi-Agent Runtime
What it is
Agno is a Python-native multi-agent runtime designed for agent collaboration and modularity. It pairs well with Agent OS for advanced observability.
Source: https://www.agno.com/
Language Support
- Python only
UI Integration
- No UI components
- Use custom React or AI SDK for frontend interfaces
Deployment Options
- Docker deployments
- Local servers or custom infrastructure
- No official serverless or hosted cloud runtime
LLM Providers
- Multi-provider
MCP Support
- MCP client support
Maturity and Adoption
- Growing quickly in Python community
- Many releases and active development
Observability
- Strong observability through Agent OS (paid)
- Advanced tracing and debugging
Cloud Lock-in Risk
- None, since deployments are fully self-managed
Best for
Python teams building multi-agent systems who need strong observability.
Key Takeaways
Agno delivers a Python-native multi-agent runtime with strong support for advanced observability through Agent OS. It is designed for collaborative and modular agent setups, offers low lock-in through self-hosted deployments, and fits teams who prefer Python and want visibility into how their agents behave.
OpenAI Agents SDK - Minimal, OpenAI-First, With Low-Code Options
What it is
The OpenAI Agents SDK provides a minimal, clean set of primitives for building agents that use OpenAI models, tools, and handoffs. It is tightly integrated with the OpenAI ecosystem and supports low-code creation.
Source: https://platform.openai.com/docs/guides/agents-sdk
Language Support
- TypeScript and Python
UI Integration
- ChatKit for drop-in embedded chat UIs
- Manual React integration possible
- Agent Builder for visual agent creation inside the OpenAI platform
Deployment Options
- Runs on your backend
- Can execute inside ChatGPT Apps
- No dedicated standalone runtime
LLM Providers
- Primarily OpenAI models, with non-native support for other LLM providers
MCP Support
- MCP compatible
Maturity and Adoption
- New but fast-growing
- Strong momentum due to ChatGPT app integrations
Observability
- Built-in OpenAI Platform tracing
- Less flexible than other third-party options like Langfuse or LangSmith
Cloud Lock-in Risk
- High, strongly tied to OpenAI models and platform rules
Best for
Teams building OpenAI-first agents who want visual tooling and fast ChatGPT integration.
Key Takeaways
The OpenAI Agents SDK provides the simplest path to building agents powered by OpenAI models, with strong support for ChatKit and low-code development through Agent Builder. Its tight integration with the OpenAI ecosystem makes it easy to get started and ideal for teams already committed to OpenAI. However, this also means the customizability is limited, both in terms of UI flexibility and deeper agent behavior, especially compared to more open or framework-agnostic alternatives.
Google ADK - Enterprise, Modular, Cloud-Native
What it is
Google's Agent Development Kit is a modular framework for building resilient agent architectures with strong delegation patterns. It is optimized for Google Cloud.
Source: https://google.github.io/adk-docs/
Language Support
- Python, Java, Go (no JavaScript option)
UI Integration
- No built-in UI
- Example UI in Angular
- Weak React and Next.js alignment
Deployment Options
- Google Cloud Agent Engine
- Cloud Run
- Docker
LLM Providers
- Multi-provider
MCP Support
- Can run MCP servers
Maturity and Adoption
- High maturity due to Google backing, specially the Python SDK
- Enterprise-grade architecture
Observability
- Uses Google Cloud Trace
Cloud Lock-in Risk
- Moderate if using Agent Engine
- None if self-hosting
Best for
Enterprises building multi-agent systems on Google Cloud.
Key Takeaways
Google ADK is designed for enterprise environments and teams deeply invested in GCP, offering a modular architecture for resilient, scalable multi-agent systems backed by strong observability through Cloud Trace. However, it currently does not offer a JavaScript or TypeScript SDK, limiting its appeal for JavaScript-first companies. It is strongest within Google’s ecosystem but less compelling outside of it.
Cloudflare Agents SDK - Infrastructure for Agents
What it is
Cloudflare Agents SDK is an infrastructure layer that provides a global, low-latency runtime for agent execution. It is not a high-level agent framework but rather the place where agents can run.
Source: https://agents.cloudflare.com/
Language Support
- TypeScript only
UI Integration
- Some client-side hooks
- No full UI library
- Requires external UI frameworks like AI SDK
Deployment Options
- Cloudflare Workers
- Global edge execution
- Durable Objects, KV, and D1 for storage
LLM Providers
- Multi-provider
MCP Support
- Very strong MCP support
- Excellent option for deploying MCP servers
Maturity and Adoption
- Younger project
- Weak documentation
- More infra than framework
Observability
- Basic Cloudflare logs
- No built-in agent tracing
Cloud Lock-in Risk
- High, since it depends fully on Cloudflare infrastructure
Best for
Infrastructure teams that need global agent execution, not a full framework.
Key Takeaways
Cloudflare’s agent tools offer extremely fast, globally distributed execution and excellent MCP server support, but minimal high-level abstractions for building agents. It is more infrastructure layer than framework, best suited for teams prioritizing edge performance and not requiring deep UI or workflow features.
Side-by-Side Comparison of All Frameworks
To make the comparison clearer, here is a quick overview of how each framework aligns with the criteria defined above.
| Framework | Language | UI Integration | Deployment Options | LLM Flexibility | Observability | Cloud Lock-in Risk |
|---|---|---|---|---|---|---|
| AI SDK (Vercel) | TypeScript | Excellent (AI Elements, Next.js, React Native) | Node, Vercel, Serverless | Multi-provider | No native (3rd-party integrations) | Low |
| Mastra | TypeScript | Limited (no native UI) | Mastra Cloud, Node, Serverless, Docker, Cloud Providers | Multi-provider | Basic built-in + 3rd-party | Moderate (if using Mastra Cloud) |
| LangGraph / LangChain | Python & TypeScript | Minimal (no UI) | LangSmith Cloud, limited self-host | Multi-provider | Excellent (LangSmith) | High |
| Agno | Python | Minimal | Docker, local servers, custom infra | Multi-provider | Strong (Agent OS) | Low |
| OpenAI Agents SDK | TypeScript & Python | Good (ChatKit, manual React) | Backend servers, ChatGPT Apps | Primarily OpenAI | Built-in OpenAI tracing | High |
| Google ADK | Python, Java, Go | Minimal | Agent Engine, Cloud Run, Docker | Multi-provider | Cloud Trace | Moderate–High |
| Cloudflare Agents SDK | TypeScript | Very minimal | Cloudflare Workers, Edge, Durable Objects | Multi-provider | Basic logging only | High |
Conclusion: Our Choice
Each framework shines in specific scenarios, and the right choice depends on UI needs, workflow complexity, cloud strategy, and language preferences.
At FASHN, we decided to use AI SDK because we needed:
- strong Next.js integration
- complete control over the UI and user interactions
- first-class React support
- rich UX patterns such as image uploads, parameter tweaking, and real-time streaming
AI SDK provided the best combination of flexibility, UI capabilities, and production readiness for our dashboard agent and for the future evolution of our platform.
If you are exploring agent frameworks, we hope this guide helps you choose the right foundation. For us at FASHN, it also sets the stage for the next generation of our fashion-focused AI agent, built to understand products, styling, and the unique workflows of the fashion industry.